Aggregating Local Descriptors for Epigraphs Recognition
DOI:
https://doi.org/10.55630/dipp.2014.4.6Keywords:
Epigraphs Recognition, Object Recognition, Content-Base Image Retrieval, Bag-of-Features, VLADAbstract
In this paper, we consider the task of recognizing epigraphs in images such as photos taken using mobile devices. Given a set of 17,155 photos related to 14,560 epigraphs, we used a k-NearestNeighbor approach in order to perform the recognition. The contribution of this work is in evaluating state-ofthe-art visual object recognition techniques in this specific context. The experimental results conducted show that Vector of Locally Aggregated Descriptors obtained aggregating SIFT descriptors is the best choice for this task.References
G. Amato, P. Bolettieri, F. Falchi and C. Gennaro, "Large Scale Image Retrieval Using Vector of Locally Aggregated Descriptors," in Similarity Search and Applications, vol. 8199, N. Brisaboa, O. Pedreira and P. Zezula, Eds., Springer Berlin Heidelberg, 2013, pp. 245-256.
G. Amato, F. Falchi and C. Gennaro, "Geometric consistency checks for kNN based image classification relying on local features," in SISAP '11: Fourth International Conference on Similarity Search and Applications, SISAP 2011, Lipari Island, Italy, June 30 - July 01, 2011, 2011.
G. Amato, F. Falchi and C. Gennaro, "On Reducing the Number of VisualWords in the Bag-of-Features Representation," in VISAPP 2013 - Proceedings of the International Conference on Computer Vision Theory and Applications, 2013.
S. Dudani, "The Distance-Weighted K-Nearest-Neighbour Rule," IEEE Transactions on Systems, Man and Cybernetics, Vols. SMC-6(4), pp. 325-327, 1975.
H. Jégou, M. Douze, C. Schmid and P. Pérez, "Aggregating local descriptors into a compact image representation," in Computer Vision and Pattern Recognition (CVPR), 2010 IEEE Conference on, 2010.
H. Jégou, F. Perronnin, M. Douze, J. Sànchez, P. Pérez and C. Schmid, "Aggregating local image descriptors into compact codes," IEEE Transactions on Pattern Analysis and Machine Intelligence, Sep 2012.
D. G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints," International Journal of Computer Vision, vol. 60, no. 2, pp. 91-110, 2004.
K. Mikolajczyk and C. Schmid, "A performance evaluation of local descriptors," Pattern Analysis and Machine Intelligence, IEEE Transactions on, vol. 27, no. 10, pp. 1615-1630, oct. 2005.
G. Salton and M. J. McGill, Introduction to Modern Information Retrieval, New York, NY, USA: McGraw-Hill, Inc., 1986.
J. Sivic and A. Zisserman, "Video Google: A Text Retrieval Approach to Object Matching in Videos," in Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2, Washington, DC, USA, 2003.
T. Tuytelaars and K. Mikolajczyk, "Local invariant feature detectors: a survey," Found. Trends. Comput. Graph. Vis., vol. 3, no. 3, pp. 177-280, 2008.
X. Zhang, Z. Li, L. Zhang, W. Y. Ma and H. Y. Shum, "Efficient indexing for large scale visual search," in Computer Vision, 2009 IEEE 12th International Conference on, 2009.
Y. Zheng, M. Zhao, Y. Song, H. Adam, U. Buddemeier, A. Bissacco, F. Brucher, T. S. Chua and H. Neven, "Tour the world: Building a web-scale landmark recognition," in CVPR, 2009.
